JOURNAL ARTICLE

Local Agglomeration and Household Mortgage Debt.

  • Published In: Management Science (INFORMS), 2025, v. 71, n. 6. P. 5001 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Chen, Fengqin Freya; Saffar, Walid; Shang, Longfei 3 of 3

Abstract

This article investigates the impact of local agglomeration—defined as the concentration of firms in the same industry within a geographic area—on household mortgage loans in the United States. Using detailed household-level data from the Survey of Income and Program Participation (SIPP) and other sources spanning 1984 to 2022, the study finds that households employed in locally agglomerated economies tend to have higher mortgage loan amounts and a greater likelihood of holding mortgage debt. The increase in mortgage loans is attributed to both higher housing demand (larger house purchases) and more favorable mortgage supply conditions (e.g., higher approval rates), driven by enhanced career prospects and downside protection in dense labor markets. Empirical strategies including difference-in-differences and instrumental variable analyses support a causal interpretation, showing that local agglomeration positively affects mortgage borrowing, particularly for skilled workers and those facing employment risks. The findings highlight the significance of local labor market composition as a geographic factor influencing household mortgage debt beyond traditional individual or macroeconomic determinants.

Additional Information

  • Source:Management Science (INFORMS). 2025/06, Vol. 71, Issue 6, p5001
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2022.03486
  • Accession Number:187706350
  • Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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